Journal articles: 'Web information retrieval'

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Thada, Vikas, and Vivek Jaglan. "Web Information Retrieval." International Journal of Computer Applications 76, no. 1 (August 23, 2013): 29–32. http://dx.doi.org/10.5120/13213-0595.

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P, Dr Natarajan, and Sonali Pandey. "INFORMATION RETRIEVAL AND WEB SEARCH." International Research Journal of Computer Science 8, no. 8 (August 30, 2021): 215–20. http://dx.doi.org/10.26562/irjcs.2021.v0808.010.

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This article discusses Web search engines; mainly the challenges in indexing the World Wide Web, the user behaviour, and the ranking factors used by these engines. Ranking factors are divided into query-dependent and query-independent factors, the latter of which have become more and more important within recent years. The possibilities of these factors are limited, mainly of those that are based on the widely used link popularity measures. The article concludes with an overview of factors that should be considered to determine the quality of Web search engines.

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Gharat, Jyotsna, and Jayant Gadge. "Web Information Retrieval using WordNet." International Journal of Computer Applications 56, no. 13 (October 20, 2012): 37–42. http://dx.doi.org/10.5120/8955-3151.

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Gharat, Jyotsna, and Jayant Gadge. "Concept based Web Information Retrieval." International Journal of Applied Information Systems 4, no. 5 (October 10, 2012): 25–29. http://dx.doi.org/10.5120/ijais12-450713.

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Sharma, Dilip Kumar, and A. K. Sharma. "Deep Web Information Retrieval Process." International Journal of Information Technology and Web Engineering 5, no. 1 (January 2010): 1–22. http://dx.doi.org/10.4018/jitwe.2010010101.

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Web crawlers specialize in downloading web content and analyzing and indexing from surface web, consisting of interlinked HTML pages. Web crawlers have limitations if the data is behind the query interface. Response depends on the querying party’s context in order to engage in dialogue and negotiate for the information. In this article, the authors discuss deep web searching techniques. A survey of technical literature on deep web searching contributes to the development of a general framework. Existing frameworks and mechanisms of present web crawlers are taxonomically classified into four steps and analyzed to find limitations in searching the deep web.

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Pokorny, J. "Web searching and information retrieval." Computing in Science and Engineering 6, no. 4 (July 2004): 43–48. http://dx.doi.org/10.1109/mcse.2004.24.

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Curran, Kevin, Cliona Murphy ., and Stephen Annesley . "Web Intelligence in Information Retrieval." Information Technology Journal 3, no. 2 (April 15, 2004): 196–201. http://dx.doi.org/10.3923/itj.2004.196.201.

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Frikh, Bouchra, and Brahim Ouhbi. "Web Algorithms for Information Retrieval." International Journal of Mobile Computing and Multimedia Communications 6, no. 1 (January 2014): 1–16. http://dx.doi.org/10.4018/ijmcmc.2014010101.

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The World Wide Web has emerged to become the biggest and most popular way of communication and information dissemination. Every day, the Web is expending and people generally rely on search engine to explore the web. Because of its rapid and chaotic growth, the resulting network of information lacks of organization and structure. It is a challenge for service provider to provide proper, relevant and quality information to the internet users by using the web page contents and hyperlinks between web pages. This paper deals with analysis and comparison of web pages ranking algorithms based on various parameters to find out their advantages and limitations for ranking web pages and to give the further scope of research in web pages ranking algorithms. Six important algorithms: the Page Rank, Query Dependent-PageRank, HITS, SALSA, Simultaneous Terms Query Dependent-PageRank (SQD-PageRank) and Onto-SQD-PageRank are presented and their performances are discussed.

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Kobayashi, Mei, and Koichi Takeda. "Information retrieval on the web." ACM Computing Surveys 32, no. 2 (June 2000): 144–73. http://dx.doi.org/10.1145/358923.358934.

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Yang, Kiduk. "Information retrieval on the web." Annual Review of Information Science and Technology 39, no. 1 (October 18, 2006): 33–80. http://dx.doi.org/10.1002/aris.1440390109.

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Buarki, Hanadi, and Bashaer Alkhateeb. "Use of hashtags to retrieve information on the web." Electronic Library 36, no. 2 (April 3, 2018): 286–304. http://dx.doi.org/10.1108/el-01-2017-0011.

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Purpose This paper aims to find out how people use hashtags as a medium of information retrieval and dissemination, and how they are used in social media tools, such as Instagram. Design/methodology/approach A quantitative question estimated the participants’ use of the hashtags during the workshop. Statistical data of the participants and their posts were collected from social network analysis tools. The posts that included the workshop’s designated hashtags were retrieved, recorded, coded and analysed to collect qualitative data. Findings In total, 74 (46 per cent) participants used the workshop’s hashtags to share posts, the retrieval of the hashtags declined by time and Google search engine retrieved the maximum results. It was found that a hashtag would be common when associated with descriptors, and that its use depends on its popularity, followers and its survival time. Finally, hashtags connect people, allow them to express their enthusiasm to reveal common interests and networks them through social media tools such as Instagram. Research limitations/implications The research limitations were in relation to the participants’ demographic information, the non-identification of their gender and hashtags being misspelt. Practical implications The research project summarises the experiences that social media has made connecting easier through the right use of hashtags by providing 24/7 free feedback, the possibility to exchange ideas and by their involvement in promoting and organising events. It also indicates interaction among people sharing the same interest by retrieving subject-based hashtags. Originality/value When retrieving information related to hashtags, it is recommended that multi-retrieval systems, social media tools and search engines should be consulted and not depend on a solo system or tool. Future research is recommended in search for a multi-retrieval social media and search engine tool that standardises the use of hashtags and will retrieve information from different platforms.

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Lokhande, Kalyani, and Dhanashree Tayade. "English-Marathi Cross Language Information Retrieval System." International Journal of Advanced Research in Computer Science and Software Engineering 7, no. 8 (August 30, 2017): 112. http://dx.doi.org/10.23956/ijarcsse.v7i8.34.

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Nowadays, diﬀerent types of content in diﬀerent languages are available on World Wide Web and their usage is increasing rapidly. Cross Language Information Retrieval (CLIR) deals with retrieval of documents in another language than the language of the requested query. Various researchers worked on Cross Language Information Retrieval systems for Indian languages using diﬀerent translation approaches. There is still CLIR system to be developed which allow user to retrieve Marathi documents when English query is given. In the proposed English to Marathi Cross Language Information Retrieval system, translation is based on query translation approach. The proposed system retrieves Marathi documents depending on matching terms in query. The performance of the proposed system is improved by query pre-processing and query expansion using WordNet.

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P., Mahalakshmi. "An Art of Review on Conceptual based Information Retrieval." Webology 18, no. 1 (February 3, 2021): 51–61. http://dx.doi.org/10.14704/web/v18i1/web18026.

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Sharma, Prem Sagar, and Divakar Yadav. "Incremental Refinement of Page Ranking of Web Pages." International Journal of Information Retrieval Research 10, no. 3 (July 2020): 57–73. http://dx.doi.org/10.4018/ijirr.2020070104.

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Web-based information retrieval systems called search engines have made things easy for information seekers, but still do not provide guarantees about the relevance of the information provided to the users. Information retrieval systems provide the information to the user based on certain retrieval criteria. Due to the large size of the WWW, it is very common that a large number of documents get identified related to a particular domain. Therefore, to help users towards finding the best matching documents, a ranking mechanism is employed by the search engine. In this article, an improved architecture for an information retrieval system is proposed. The proposed system makes a query log for each user query and stores the results retrieved to the user for that query. The system also provides relevant results by analyzing the content of the pages retrieved for the user query.

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Pashaei Barbin, Javad, and Isa Maleki. "Semantic Web: Information Retrieval from World Wide Web." International Journal on Cybernetics & Informatics 2, no. 6 (December 31, 2013): 13–25. http://dx.doi.org/10.5121/ijci.2013.2602.

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K.N., Sridevi, and Prakasha S. "Doc-To-Tokens based Pre-Processing in Information Retrieval System." Webology 18, SI05 (October 30, 2021): 570–79. http://dx.doi.org/10.14704/web/v18si05/web18247.

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Mahalakshmi, P., and N. Sabiyath Fathima. "An Art of Review on Conceptual based Information Retrieval." Webology 18, SI02 (January 13, 2021): 21–31. http://dx.doi.org/10.14704/web/v18si02/web18009.

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Basically keywords are used to index and retrieve the documents for the user query in a conventional information retrieval systems. When more than one keywords are used for defining the single concept in the documents and in the queries, inaccurate and incomplete results were produced by keyword based retrieval systems. Additionally, manual interventions are required for determining the relationship between the related keywords in terms of semantics to produce the accurate results which have paved the way for semantic search. Various research work has been carried out on concept based information retrieval to tackle the difficulties that are caused by the conventional keyword search and the semantic search systems. This paper aims at elucidating various representation of text that is responsible for retrieving relevant search results, approaches along with the evaluation that are carried out in conceptual information retrieval, the challenges faced by the existing research to expatiate requirements of future research. In addition, the conceptual information that are extracted from the different sources for utilizing the semantic representation by the existing systems have been discussed.

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Mithun, Ahamed M., and Z. Abu Bakar. "Empowering Information Retrieval in Semantic Web." International Journal of Computer Network and Information Security 12, no. 2 (April 8, 2020): 41–48. http://dx.doi.org/10.5815/ijcnis.2020.02.05.

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Pruski, Cédric, Nicolas Guelfi, and Chantal Reynaud. "Adaptive Ontology-Based Web Information Retrieval." International Journal of Web Portals 3, no. 3 (July 2011): 41–58. http://dx.doi.org/10.4018/ijwp.2011070104.

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Finding relevant information on the Web is difficult for most users. Although Web search applications are improving, they must be more “intelligent” to adapt to the search domains targeted by queries, the evolution of these domains, and users’ characteristics. In this paper, the authors present the TARGET framework for Web Information Retrieval. The proposed approach relies on the use of ontologies of a particular nature, called adaptive ontologies, for representing both the search domain and a user’s profile. Unlike existing approaches on ontologies, the authors make adaptive ontologies adapt semi-automatically to the evolution of the modeled domain. The ontologies and their properties are exploited for domain specific Web search purposes. The authors propose graph-based data structures for enriching Web data in semantics, as well as define an automatic query expansion technique to adapt a query to users’ real needs. The enriched query is evaluated on the previously defined graph-based data structures representing a set of Web pages returned by a usual search engine in order to extract the most relevant information according to user needs. The overall TARGET framework is formalized using first-order logic and fully tool supported.

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Brown, Eric W., and Alan F. Smeaton. "Hypertext information retrieval for the Web." ACM SIGIR Forum 32, no. 2 (September 1998): 8–13. http://dx.doi.org/10.1145/305110.305114.

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Alhenshiri, Anwar, and James Blustein. "Exploring visualisation in web information retrieval." International Journal of Internet Technology and Secured Transactions 3, no. 3 (2011): 320. http://dx.doi.org/10.1504/ijitst.2011.041299.

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Lee, Hyuk-Jin. "Mediated information retrieval in Web searching." Proceedings of the American Society for Information Science and Technology 45, no. 1 (2008): 1–10. http://dx.doi.org/10.1002/meet.2008.1450450356.

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Kogan, S., N. Ash, R. A. Greenes, A. A. Boxwala, and Q. Zeng. "Characteristics of Consumer Terminology for Health Information Retrieval." Methods of Information in Medicine 41, no. 04 (2002): 289–98. http://dx.doi.org/10.1055/s-0038-1634490.

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Summary Objectives: As millions of consumers perform health information retrieval online, the mismatch between their terminology and the terminologies of the information sources could become a major barrier to successful retrievals. To address this problem, we studied the characteristics of consumer terminology for health information retrieval. Methods: Our study focused on consumer queries that were used on a consumer health service Web site and a consumer health information Web site. We analyzed data from the site-usage logs and conducted interviews with patients. Results: Our findings show that consumers’ information retrieval performance is very poor. There are significant mismatches at all levels (lexical, semantic and mental models) between the consumer terminology and both the information source terminology and standard medical vocabularies. Conclusions: Comprehensive terminology support on all levels is needed for consumer health information retrieval.

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Chawla, Suruchi. "Intelligent Information Retrieval Using Hybrid of Fuzzy Set and Trust." Oriental journal of computer science and technology 10, no. 2 (April 19, 2017): 311–25. http://dx.doi.org/10.13005/ojcst/10.02.09.

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The main challenge for effective web Information Retrieval(IR) is to infer the information need from user’s query and retrieve relevant documents. The precision of search results is low due to vague and imprecise user queries and hence could not retrieve sufficient relevant documents. Fuzzy set based query expansion deals with imprecise and vague queries for inferring user’s information need. Trust based web page recommendations retrieve search results according to the user’s information need. In this paper an algorithm is designed for Intelligent Information Retrieval using hybrid of Fuzzy set and Trust in web query session mining to perform Fuzzy query expansion for inferring user’s information need and trust is used for recommendation of web pages according to the user’s information need. Experiment was performed on the data set collected in domains Academics, Entertainment and Sports and search results confirm the improvement of precision.

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Priya, R. Vishnu, and A. Vadivel. "Capturing Semantics of Web Page using Weighted TAG- Tree for Information Retrieval." International Journal of Asian Business and Information Management 3, no. 4 (October 2012): 7–24. http://dx.doi.org/10.4018/jabim.2012100102.

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Web pages are highly dynamic and it’s difficult to retrieve the relevant web pages in top 10 search results. This is based on some ranking mechanism incorporated retrieval system. The Retrieval system is designed for ranking the relevant web pages for user query. Usually, the retrieval system considers many techniques for ranking such as link based, connectivity based and keyword based techniques. The authors’ rank the web pages using the keywords and its associated TAGs. Based on the importance of each TAGs, weights are assigned and the semantics of the page is captured. In addition, the semantic information is represented in compact tree form, which supports both incremental and interactive mining with refined retrieval. From the experimental result, the authors have observed that the performance of the proposed approach is encouraging compared to the recently proposed approach.

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Chkiwa, Mounira, Anis Jedidi, and Faiez Gargouri. "Semantic / Fuzzy Information Retrieval System." International Journal of Information Technology and Web Engineering 12, no. 1 (January 2017): 37–56. http://dx.doi.org/10.4018/ijitwe.2017010103.

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In this paper, the authors present an overall description of their information retrieval system which makes a practical collaboration between Semantic Web and Fuzzy logic in order to have profit from their advantages in the information retrieval domain. Their system is dedicated for kids, for this reason the semantic/fuzzy collaboration materialized must be in the background of the information retrieval process because such category of users cannot certainly control semantic web technologies neither fuzzy logic commands. In this paper, the authors present the different services proposed by their system and how they use Semantic Web and Fuzzy logic to develop it. Evaluation tests of the system using universal measures show clearly its efficiency.

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Shen, Jin Xing. "Ontology-Based Semantic Retrieval for Management Information System." Applied Mechanics and Materials 278-280 (January 2013): 2069–72. http://dx.doi.org/10.4028/www.scientific.net/amm.278-280.2069.

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In order to achieve semantic retrieval for scientific research information in WWW, this paper applies an ontology-based framework to information retrieval system for management information system. After analyze the limitations of traditional method, bring a semantic search forward, and mainly introduce the thought of the semantic retrieval as well as the way to constitute ontology entity and the language that describes it. Moreover, semantic retrieval system based on ontology is also given. The application to retrieve project information shows that the framework can overcome the localization of other ontology’s models, and this research facilitates the semantic retrieval of management information through semantic retrieval concepts on the Semantic Web.

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Liu, Na, and Kun Liu. "Mobile Agents Build Web Information Retrieval System." Applied Mechanics and Materials 543-547 (March 2014): 3373–76. http://dx.doi.org/10.4028/www.scientific.net/amm.543-547.3373.

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In recent years, mobile agent has always been the hot spot of the academic research, this paper introduces the concept of mobile agent, mobile agent system architecture and key technologies, combined with the mobile technology and web information retrieval technology, design a model of information retrieval system based on mobile agent, and expounds the key technology to realize the model needs to solve.

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John, Rini, and Sharvari Govilkar. "Information Retrieval Technique for Web Using NLP." International Journal on Natural Language Computing 6, no. 5 (October 30, 2017): 01–17. http://dx.doi.org/10.5121/ijnlc.2017.6501.

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Lewandowski, Dirk. "Web searching, search engines and Information Retrieval." Information Services & Use 25, no. 3-4 (January 25, 2006): 137–47. http://dx.doi.org/10.3233/isu-2005-253-402.

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Gadge, Jayant, S. S. Sane, and H. B. Kekre. "N-layer Approach to Web Information Retrieval." International Journal of Applied Information Systems 5, no. 1 (January 10, 2013): 45–49. http://dx.doi.org/10.5120/ijais12-450840.

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Kumar, Prashant. "Web Information Retrieval using JShop and Python." International Journal for Research in Applied Science and Engineering Technology 8, no. 6 (June 30, 2020): 1966–69. http://dx.doi.org/10.22214/ijraset.2020.6322.

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Bond, Carol S. "Web users' information retrieval methods and skills." Online Information Review 28, no. 4 (August 1, 2004): 254–59. http://dx.doi.org/10.1108/14684520410553741.

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When trying to locate information on the Web people are faced with a variety of options. This research reviewed how a group of health related professionals approached the task of finding a named document. Most were eventually successful, but the majority encountered problems in their search techniques. Even experienced Web users had problems when working with a different interface to normal, and without access to their favourites. No relationship was found between the number of years' experience Web users had and the efficiency of their searching strategy. The research concludes that if people are to be able to use the Web quickly and efficiently as an effective information retrieval tool, as opposed to a recreational tool to surf the Internet, they need to have both an understanding of the medium and the tools, and the skills to use them effectively, both of which were lacking in the majority of participants in this study.

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Islam, Anwarul, and K. C. Panda. "Web‐based information retrieval trends of researchers." Electronic Library 25, no. 6 (November 20, 2007): 757–65. http://dx.doi.org/10.1108/02640470710837173.

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PurposeThe purpose of the study is to find out the trends of web‐based information seekers at Sambalpur University, India since the internet is more helpful than the library in the present electronic era.Design/methodology/approachThe study is based on the case study method. A structured questionnaire was distributed among the relevant researchers at Sambalpur University in order to ascertain their web searching habits.FindingsThe application of is ever increasing – to the extent that people believe that electronic material will eventually replace the traditional library and users need not go there to find and collect the information they need. The study revealed, however, that in one Indian university, traditional library and printed materials are still more effective to researchers than web‐based information and resources.Originality/valueThis is an original case study on web‐based information retrieval trends of university researchers. This paper will help web researchers to obtain a clear‐cut picture of the information seeking and retrieval trends of the researchers of an Indian university which will serve as a role model for the universities of the Third World.

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Gudivada, V. N., V. V. Raghavan, W. I. Grosky, and R. Kasanagottu. "Information retrieval on the World Wide Web." IEEE Internet Computing 1, no. 5 (1997): 58–68. http://dx.doi.org/10.1109/4236.623969.

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Kushchu, I. "Web-Based Evolutionary and Adaptive Information Retrieval." IEEE Transactions on Evolutionary Computation 9, no. 2 (April 2005): 117–25. http://dx.doi.org/10.1109/tevc.2004.842093.

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Koshman, Sherry. "Visualization-based information retrieval on the Web." Library & Information Science Research 28, no. 2 (June 2006): 192–207. http://dx.doi.org/10.1016/j.lisr.2006.03.017.

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Melucci, Massimo, and David Hawking. "Introduction: A perspective on Web Information Retrieval." Information Retrieval 9, no. 2 (March 2006): 119–22. http://dx.doi.org/10.1007/s10791-006-7145-2.

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R, Surendiran, and DuraiSamy K. "An Approach in Semantic Web Information Retrieval." International Journal of Electronics and Communication Engineering 1, no. 1 (February 25, 2014): 17–21. http://dx.doi.org/10.14445/23488549/ijece-v1i1p105.

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Veeramalai, Sankaradass, and Arputharaj Kannan. "Intelligent Information Retrieval Using Fuzzy Association Rule Classifier." International Journal of Intelligent Information Technologies 7, no. 3 (July 2011): 14–27. http://dx.doi.org/10.4018/jiit.2011070102.

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As the use of web applications increases, when users use search engines for finding some information by inputting keywords, the number of web pages that match the information increases at a tremendous rate. It is not easy for a user to retrieve the exact web page which contains information he or she requires. In this paper, an approach to web page retrieval system using the hybrid combination of context based and collaborative filtering method employing the concept of fuzzy association rule classification is introduced and the authors propose an innovative clustering of user profiles in order to reduce the filtering space and achieves sub-linear filtering time. This approach can produce recommended web page links for users based on the information that associates strongly with users’ queries quickly with better efficiency and therefore improve the recall, precision of a search engine.

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B, Gomathi. "Semantic Web Application in E-learning Using Protege based on Information Retrieval." Journal of Advanced Research in Dynamical and Control Systems 12, SP7 (July 25, 2020): 1847–55. http://dx.doi.org/10.5373/jardcs/v12sp7/20202297.

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T, Padmavathi. "Comparative Analysis of Information Retrieval using Ontology Based vs Traditional Information Systems in Food Science Domain." DESIDOC Journal of Library & Information Technology 40, no. 02 (March 28, 2020): 437–44. http://dx.doi.org/10.14429/djlit.40.02.15213.

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The current methods of searching and information retrieval are imprecise, often yielding results in tens of thousands of web pages. Extraction of the actual information needed often requires extensive manual browsing of retrieved documents. In order to address these drawbacks, this paper introduces an implementation in the field of food science of the ontology-based information retrieval system, and comparison is made with conventional information systems. The ontology of Food Semantic Web Knowledge Base (FSWKB) was built using the Protégé framework which supports two main models of ontology through the editors Protégé-Frames and Protégé-OWL. The FSWKB is composed of two heterogeneous ontologies, and these are merged and processed on a separate server application making use of the Apache Jena Fuseki an SPARQL server offering SPARQL endpoint. The experimental results indicated that ontology-based information systems are more effective in terms of their retrieval capability compared to the more conventional information retrieval systems. The retrieval effectiveness was measured in terms of precision and recall. The results of the work showed that traditional search results in average precision and recall levels of 0.92 and 0.18. The ontology-based test for precision and recall has average rates of 0.96 and 0.97.

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Lacroix, Zoé. "Web data retrieval and extraction." Data & Knowledge Engineering 44, no. 3 (March 2003): 347–67. http://dx.doi.org/10.1016/s0169-023x(02)00143-x.

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Chen, Zheng, Liu Wenyin, Feng Zhang, Mingjing Li, and Hongjiang Zhang. "Web mining for Web image retrieval." Journal of the American Society for Information Science and Technology 52, no. 10 (2001): 831–39. http://dx.doi.org/10.1002/asi.1132.

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Rasmussen, Edie M. "Indexing and retrieval for the Web." Annual Review of Information Science and Technology 37, no. 1 (January 31, 2005): 91–124. http://dx.doi.org/10.1002/aris.1440370104.

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Sheng, Zhong Biao, Hua Ping Jia, and Xiao Rong Tong. "Design of Personalized Intelligent Information Retrieval Model Based on Agent." Applied Mechanics and Materials 155-156 (February 2012): 1175–79. http://dx.doi.org/10.4028/www.scientific.net/amm.155-156.1175.

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The features of vast distributed dynamic information on Web caused the problem of “overload” and “mislead” while query. Intelligent agent is a way to solve it. After considering the problems of users’ personal interests during the information retrieve adequately, the paper proposes an intelligent information retrieval model based-on Agent. This system integrated domain knowledge and used many arithmetic of learning user’s interest. Each Agent co-operates to finish information retrieval task, manifest the characteristics of intellectualization and individuality of in information retrieval. It is a good way to realize the highly effective intelligent retrieval system research.

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Rahayuda, I. Gede Surya, and Ni Putu Linda Santiari. "Web Harvesting for Data Retrieval on Scientific Journal Sites." Jurnal Informatika Universitas Pamulang 6, no. 1 (March 31, 2021): 202. http://dx.doi.org/10.32493/informatika.v6i1.10077.

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Publishing scientific articles online in journals is a must for researchers or academics. In choosing the journal of purpose, the researcher must look at important information on the journal's web, such as indexing, scope, fee, quarter and other information. This information is generally not collected in one page, but spread over several pages in a web journal. This will be complicated when researchers have to look at information in several journals, moreover, the information in these journals may change at any time. In this research, web harvesting design is conducted to retrieve information on web journals. With web harvesting, information that is spread across several pages can be collected into one, and researchers do not need to worry if the information has changed, because the information collected is the last or updated information. Harvesting technique is done by taking the page URL of the page, starting the source code from where the information is retrieved and end source code until the information stops being retrieved. Harvesting technique was successfully developed based on the web bootstrap framework. The test data is taken from several scientific journal webs. The information collected includes name, description, accreditation, indexing, scope, publication rate, publication charge, template and quarter. Based on tests carried out using black box testing, it is known that all the features made are as expected.

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Kong, Shao Ying, and Hong Liang Guo. "Research on Intelligent Information Retrieval Based on Semantic Web Technology." Applied Mechanics and Materials 380-384 (August 2013): 2075–78. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.2075.

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According to the rapid development of the Internet and the increase of the network resources, it was very important to search the information resources for meeting the need of large ground of users. The key words were used as the indexes for retrieval with traditional method. But it couldnt reflect the real purposes and intentions of the users precisely. An improved information retrieval method based on the semantic web technology was proposed for carrying out the information retrieval work precisely and effectively. The mew retrieval system was not just recognizing the words simply, but it could comprehend the semantic ingredients of the words and sentences. The semantic web retrieval system was constructed eventually. Comparing to the traditional keywords retrieval method, the similarity degree was calculated in the simulation, simulation result shows that the retrieval precision ratio of new method increases by 15%, then the intelligent, efficient and accurate information retrieval system is realized successfully.

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Xu, Ming, and Yun Ke. "The Research of Information Retrieval Technology Based on Semantic Analysis." Advanced Materials Research 926-930 (May 2014): 2160–63. http://dx.doi.org/10.4028/www.scientific.net/amr.926-930.2160.

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Abstract:

The common information retrieval technology is mainly based on keyword matching and this kind of method only focuse on the optimization algorithm, ignoring the semantic research. This does not solve the fundamental semantic multiplicity, retrieve diversity, related web undetected, sort unstandardized. This paper is a study of these problems arise for the current proposed MIRSA information retrieval model based on semantic analysis. This model consists of the following four main key points: disambiguation method, semantic expansion algorithm, the search terms match strategy, web sorting algorithms. This model can effectively solve the problem of semantic multiplicity, avoid missed relevant pages and reasonably improve the sor of related pages.

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ZAKOS, JOHN, and BRIJESH VERMA. "CONCEPT-BASED TERM WEIGHTING FOR WEB INFORMATION RETRIEVAL." International Journal of Computational Intelligence and Applications 06, no. 02 (June 2006): 193–207. http://dx.doi.org/10.1142/s1469026806001915.

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Abstract:

In this paper we present a novel technique for determining term importance by exploiting concept-based information found in ontologies. Calculating term importance is a significant and fundamental aspect of most information retrieval approaches, and it is traditionally determined through inverse document frequency (IDF). We propose concept-based term weighting (CBW), a technique that is fundamentally different to IDF in that it calculates term importance by intuitively interpreting the conceptual information in ontologies. We show that when CBW is used in an approach for web information retrieval on benchmark data, it performs comparatively to IDF, with only a 3.5% degradation in retrieval accuracy. While this small degradation has been observed, the significance of this technique is that (1) unlike IDF, CBW is independent of document collection statistics, (2) it presents a new way of interpreting ontologies for retrieval, and (3) it introduces an additional source of term importance information that can be used for term weighting.

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Journal articles on the topic 'Web information retrieval'

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